sangeranalyseR

Fast, flexible, and reproducible workflows for assembling Sanger sequencing data into contigs in R. A free and open-source alternative to Geneious, CodonCode Aligner, and Phred-Phrap-Consed.

For full documentation see 📒 sangeranalyseR Documentation.

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What’s new

• ~1.7× faster SangerAlignment(...) thanks to a C++ peak-detection inner loop, parallel per-read construction (BiocParallel), and lazy 3-frame amino-acid translation that only runs when you ask for it.
• Interactive Plotly + WebGL chromatograms via the new chromatogram_plotly() — smooth scrolling and zoom on Sanger traces with tens of thousands of points.
• Global trimming dashboard via the new globalTrimApp(sa) — adjust M1 / M2 trimming parameters across an entire SangerAlignment with a live consensus preview.

For the full per-version changelog see NEWS.md.

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Installation

From Bioconductor (recommended)

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# Stable release
BiocManager::install("sangeranalyseR")

# Development branch (latest features)
BiocManager::install(version = "devel")
BiocManager::install("sangeranalyseR")

From GitHub

# install.packages("devtools")    # if needed
devtools::install_github("roblanf/sangeranalyseR", ref = "devel")

System requirements

• R ≥ 4.0.0 (kept intentionally permissive for institutional installs).
• macOS, Linux, or Windows.
• C++17 toolchain for the Rcpp module — pre-installed on macOS (Xcode CLT), Linux (build-essential), and Windows (Rtools).
• Optional: pandoc for HTML report rendering.

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Quick start

A four-step end-to-end example using the bundled Allolobophora chlorotica fixture (8 ABIF files, 4 contigs).

1. Load and assemble

library(sangeranalyseR)

# Locate the bundled fixture
ab1_dir <- system.file("extdata", "Allolobophora_chlorotica", "ACHLO",
                       package = "sangeranalyseR")

# Build the alignment — uses lazy AA + BiocParallel by default
sa <- SangerAlignment(
    inputSource         = "ABIF",
    processMethod       = "REGEX",
    ABIF_Directory      = ab1_dir,
    REGEX_SuffixForward = "_[0-9]*_F.ab1$",
    REGEX_SuffixReverse = "_[0-9]*_R.ab1$",
    TrimmingMethod      = "M1",
    M1TrimmingCutoff    = 0.0001,
    BPPARAM             = BiocParallel::bpparam()
)

sa@objectResults@creationResult       # TRUE
length(sa@contigList)                  # 4 contigs
length(sa@contigsConsensus)            # cross-contig consensus length

2. Tweak trimming parameters interactively

# Open the global trim dashboard — sliders for M1 / M2 with live preview.
# Returns the re-trimmed SangerAlignment when the user clicks "Done".
sa2 <- globalTrimApp(sa)

3. Explore your data

Open the per-read Shiny app for a SangerContig (or use launchApp(sa) on a full SangerAlignment):

launchApp(sa)

You can also pull up an interactive WebGL chromatogram for a single read without launching the full app:

sr <- sa@contigList[[1]]@forwardReadList[[1]]

chromatogram_plotly(sr, max_points = 8000, showtrim = TRUE)

4. Export and report

# FASTA: dispatches across SangerRead / SangerContig / SangerAlignment
writeFasta(sa)

# HTML report (requires pandoc) — works on lazy-AA objects out of the box
generateReport(sa)

Lazy AA accessors

When no AA reference is supplied, primaryAASeqS{1,2,3}() compute on demand:

sr <- sa@contigList[[1]]@forwardReadList[[1]]
length(sr@primaryAASeqS1)    # 0  — slot is empty under lazyAA = TRUE
primaryAASeqS1(sr)            # AAString returned by accessor (computed on call)

To restore eager translation (e.g. for the legacy direct-slot pattern):

sr_eager <- SangerRead(
    inputSource    = "ABIF",
    readFeature    = "Forward Read",
    readFileName   = file.path(ab1_dir, "Achl_ACHLO006-09_1_F.ab1"),
    TrimmingMethod = "M1",
    lazyAA         = FALSE
)
sr_eager@primaryAASeqS1       # populated at construction time

Cross-platform parallel

# macOS / Linux: forks via MulticoreParam
sa <- SangerAlignment(..., BPPARAM = BiocParallel::MulticoreParam(workers = 4))

# Windows: cluster-of-processes via SnowParam
sa <- SangerAlignment(..., BPPARAM = BiocParallel::SnowParam(workers = 4))

# Or just register a default once and forget about it:
BiocParallel::register(BiocParallel::SerialParam())
sa <- SangerAlignment(...)    # picks up the registered backend

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Citation

If sangeranalyseR is useful in your published work, please cite:

Kuan-Hao Chao, Kirston Barton, Sarah Palmer, and Robert Lanfear (2021). sangeranalyseR: simple and interactive processing of Sanger sequencing data in R. Genome Biology and Evolution. DOI: 10.1093/gbe/evab028.

Available on Genome Biology and Evolution (GBE) and Bioconductor.

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Maintainers

• Kuan-Hao Chao <ntueeb05howard@gmail.com> (creator, maintainer)
• Rob Lanfear <rob.lanfear@gmail.com> (creator, author)

License: GPL-2 (see LICENSE).

Issues and feature requests: github.com/roblanf/sangeranalyseR/issues.